Note: Open Rproj first, then script. To easily use relative
paths, click the down button next to knit and then click “Knit Directory
–> Project Directory”. This should make loading and saving files much
easier.
1. Load packages and input data
Load packages
library(edgeR, quietly = TRUE) #edgeR-v3.30.3
library(vegan, quietly = TRUE)
library(Dune, quietly = TRUE)
library(ggplot2, quietly = TRUE) #ggplot2-v3.3.5
library(tidyverse, quietly = TRUE) #tidyverse-v1.3.1
library(Rmisc, quietly = TRUE)
library(mixOmics, quietly = TRUE)
#library(ggridges)
#library(hrbrthemes)
#library(viridis)
Load the input file containing the treatment information
#treatment information
treatmentinfo <- read.csv("Sample_Info/samples_Pacuta.annotations.txt", header = TRUE, sep = "\t", fileEncoding="UTF-8-BOM") #read in file
top3groups <- c("Group2", "Group3", "Group6")
treatmentinfo <- filter(treatmentinfo, group%in%top3groups)
table(treatmentinfo$group)
Group2 Group3 Group6
34 24 27
Load the input file containing the gene count matrix
#gene count matrix
gcount <- as.data.frame(read_delim("Genome_Info/Pocillopora_acuta_KBHIv2.gentrome.fa.gz.salmon.numreads.matrix", delim = "\t", col_names = TRUE, show_col_types = FALSE), fileEncoding="UTF-8-BOM") #read in file
rownames(gcount) <- gcount$Name #makes "Name" the rowname
gcount <- gcount[,-c(1)] #drops the "Name" column
gcount <- round(gcount) #round
dim(gcount); head(gcount)[,1:3] #view dataset attributes
[1] 33259 119
gcount <- gcount[,treatmentinfo$sample]
Determine library size
libSize.df <- data.frame(libSize=colSums(gcount))
Make DGE object
DGEdat <- DGEList(counts=as.matrix(gcount), samples=treatmentinfo,
group=treatmentinfo$temp)
dim(DGEdat$counts)
[1] 33259 85
2. Pre-filtering
lib.sizes.cpm <- colSums(gcount)/1000000
lib.sizes.cpm <- tibble(sample=colnames(gcount), lib.size=lib.sizes.cpm)
keep <- rowSums(cpm(gcount) > 3.33) >= 2
table(keep)
keep
FALSE TRUE
12211 21048
DGEdat <- DGEdat[keep, , keep.lib.sizes=FALSE]
3. Data normalization
DGEdat <- calcNormFactors(DGEdat)
DGEdat$samples
4. Plot global gene expression
Log transform the counts matrix for the next plots
DGEdat.cpm <- DGEdat #make a copy the edgeR dataset
DGEdat.cpm$counts <- cpm(DGEdat.cpm$counts, log=TRUE, prior.count=5) #log transform the copy for the next plots
Run a principle coordinates analysis, all samples
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe containing all plotting info
#d <- filter(d, temp=="Hot")
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
ncol(DGEdat.cpm$counts[,d$sample])
[1] 85
# Partial Least Squares Analysis
mixOmic_plsda <- plsda(t(DGEdat.cpm$counts[,d$sample]), d$timename, ncomp = 85)
plotVar(mixOmic_plsda, plot = F)
plsda_plot <- biplot(mixOmic_plsda, var.axes = TRUE, cutoff = .8, ind.names = F); plsda_plot

plotIndiv(mixOmic_plsda, ind.names = F, ellipse = TRUE, legend = TRUE)

Run a principle coordinates analysis, Hot samples
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe containing all plotting info
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
d <- filter(d, temp=="Hot")
ncol(DGEdat.cpm$counts[,d$sample])
[1] 44
# Partial Least Squares Analysis
mixOmic_plsda <- plsda(t(DGEdat.cpm$counts[,d$sample]), d$TP_temp, ncomp = 44)
plotVar(mixOmic_plsda, plot = F)
plsda_plot <- biplot(mixOmic_plsda, var.axes = TRUE, cutoff = .8, ind.names = F); plsda_plot

plotIndiv(mixOmic_plsda, ind.names = F, ellipse = TRUE, legend = TRUE)

Run a principle coordinates analysis, Amb samples
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe containing all plotting info
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
d <- filter(d, temp=="Amb")
ncol(DGEdat.cpm$counts[,d$sample])
[1] 41
# Partial Least Squares Analysis
mixOmic_plsda <- plsda(t(DGEdat.cpm$counts[,d$sample]), d$TP_temp, ncomp = 41)
plotVar(mixOmic_plsda, plot = F)
plsda_plot <- biplot(mixOmic_plsda, var.axes = TRUE, cutoff = .8, ind.names = F); plsda_plot

plotIndiv(mixOmic_plsda, ind.names = F, ellipse = TRUE, legend = TRUE)

Emma script
# set working directory and load necessary packages
library(reshape2)
#library(ggbiplot)
library(broom) # devtools::install_github("tidymodels/broom")
library(cowplot)
library(ggpubr)
#library(ggfortify)
library(ggrepel)
library(gridExtra)
#library(ggforce)
# set seed
set.seed(54321)
Load datasets
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
#PCA
Mcap.pca.out <- prcomp(t(DGEdat.cpm$counts[,d$sample])) #, center=FALSE, scale=FALSE) #run PCA
M.summary <- summary(Mcap.pca.out); M.summary #view results
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Standard deviation 47.8457 39.3804 31.8471 23.44963 19.12449 16.68497 15.27388 14.09774 12.78940 11.69200 11.33499 10.95941 10.53431 10.13147 9.45036
Proportion of Variance 0.2376 0.1609 0.1053 0.05707 0.03796 0.02889 0.02421 0.02063 0.01698 0.01419 0.01333 0.01247 0.01152 0.01065 0.00927
Cumulative Proportion 0.2376 0.3985 0.5038 0.56085 0.59881 0.62770 0.65191 0.67253 0.68951 0.70370 0.71703 0.72950 0.74101 0.75166 0.76093
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 PC31 PC32
Standard deviation 9.07542 8.86547 8.54723 8.36652 8.11764 8.07213 7.72604 7.4751 7.33188 6.98285 6.81872 6.75749 6.59476 6.50150 6.39636 6.34873 6.27945
Proportion of Variance 0.00855 0.00816 0.00758 0.00726 0.00684 0.00676 0.00619 0.0058 0.00558 0.00506 0.00483 0.00474 0.00451 0.00439 0.00425 0.00418 0.00409
Cumulative Proportion 0.76948 0.77764 0.78522 0.79248 0.79932 0.80609 0.81228 0.8181 0.82366 0.82872 0.83354 0.83828 0.84280 0.84718 0.85143 0.85561 0.85970
PC33 PC34 PC35 PC36 PC37 PC38 PC39 PC40 PC41 PC42 PC43 PC44 PC45 PC46 PC47 PC48 PC49
Standard deviation 6.15489 6.07861 6.03575 5.97942 5.90674 5.84883 5.79111 5.76026 5.70003 5.66802 5.6388 5.61025 5.55841 5.49090 5.45282 5.42537 5.36089
Proportion of Variance 0.00393 0.00383 0.00378 0.00371 0.00362 0.00355 0.00348 0.00344 0.00337 0.00333 0.0033 0.00327 0.00321 0.00313 0.00309 0.00305 0.00298
Cumulative Proportion 0.86364 0.86747 0.87125 0.87496 0.87858 0.88213 0.88561 0.88906 0.89243 0.89576 0.8991 0.90233 0.90554 0.90866 0.91175 0.91481 0.91779
PC50 PC51 PC52 PC53 PC54 PC55 PC56 PC57 PC58 PC59 PC60 PC61 PC62 PC63 PC64 PC65 PC66
Standard deviation 5.33055 5.26422 5.23060 5.20470 5.1901 5.13711 5.12038 5.06910 5.04595 5.01429 4.99416 4.95214 4.93965 4.89052 4.84779 4.8065 4.78845
Proportion of Variance 0.00295 0.00288 0.00284 0.00281 0.0028 0.00274 0.00272 0.00267 0.00264 0.00261 0.00259 0.00255 0.00253 0.00248 0.00244 0.0024 0.00238
Cumulative Proportion 0.92074 0.92361 0.92645 0.92926 0.9321 0.93480 0.93752 0.94019 0.94283 0.94544 0.94803 0.95057 0.95310 0.95559 0.95802 0.9604 0.96280
PC67 PC68 PC69 PC70 PC71 PC72 PC73 PC74 PC75 PC76 PC77 PC78 PC79 PC80 PC81 PC82 PC83
Standard deviation 4.76581 4.74457 4.71724 4.67894 4.63921 4.58973 4.55821 4.53962 4.49122 4.47088 4.40430 4.37397 4.34636 4.2770 4.24909 4.21365 4.1665
Proportion of Variance 0.00236 0.00234 0.00231 0.00227 0.00223 0.00219 0.00216 0.00214 0.00209 0.00207 0.00201 0.00199 0.00196 0.0019 0.00187 0.00184 0.0018
Cumulative Proportion 0.96516 0.96749 0.96980 0.97208 0.97431 0.97650 0.97865 0.98079 0.98288 0.98496 0.98697 0.98896 0.99092 0.9928 0.99469 0.99653 0.9983
PC84 PC85
Standard deviation 4.00645 5.257e-14
Proportion of Variance 0.00167 0.000e+00
Cumulative Proportion 1.00000 1.000e+00
biplot(Mcap.pca.out) #plot results


Mcapitata.all <- Mcap.pca.out %>%
augment(d) %>% # add original dataset back in
group_by(timename, temp) %>%
mutate(PC5.mean = mean(.fittedPC5),
PC6.mean = mean(.fittedPC6))
mcap.Hot.segments <- Mcapitata.all %>% subset(temp == "Hot") %>%
select(timename, temp, PC5.mean, PC6.mean) %>%
gather(variable, value, -(timename:temp)) %>%
unite(group, timename, variable) %>% distinct() %>%
spread(group, value)
mcap.amb.segments <- Mcapitata.all %>% subset(temp == "Amb") %>%
select(timename, temp, PC5.mean, PC6.mean) %>%
gather(variable, value, -(timename:temp)) %>%
unite(group, timename, variable) %>% distinct() %>%
spread(group, value)

---
title: "PLS-DA of *P. acuta* gene expression"
author: "Erin Chille"
date: "05/09/2024"
output:
  pdf_document: default
  html_notebook: default
---


```{r setup, include=FALSE}
rm(list = ls()) #clear environment
```

*Note: Open Rproj first, then script. To easily use relative paths, click the down button next to knit and then click "Knit Directory --> Project Directory". This should make loading and saving files much easier.*

## 1. Load packages and input data

Load packages
```{r, message=FALSE, warning=FALSE}
library(edgeR, quietly = TRUE) #edgeR-v3.30.3
library(vegan, quietly = TRUE)
library(Dune, quietly = TRUE)
library(ggplot2, quietly = TRUE) #ggplot2-v3.3.5
library(tidyverse, quietly = TRUE) #tidyverse-v1.3.1
library(Rmisc, quietly = TRUE)
library(mixOmics, quietly = TRUE)
#library(ggridges)
#library(hrbrthemes)
#library(viridis)

```

Load the input file containing the treatment information
```{r}
#treatment information
treatmentinfo <- read.csv("Sample_Info/samples_Pacuta.annotations.txt", header = TRUE, sep = "\t", fileEncoding="UTF-8-BOM") #read in file
top3groups <- c("Group2", "Group3", "Group6")
treatmentinfo <- filter(treatmentinfo, group%in%top3groups)
table(treatmentinfo$group)
```

Load the input file containing the gene count matrix
```{r}
#gene count matrix
gcount <- as.data.frame(read_delim("Genome_Info/Pocillopora_acuta_KBHIv2.gentrome.fa.gz.salmon.numreads.matrix", delim = "\t", col_names = TRUE, show_col_types = FALSE), fileEncoding="UTF-8-BOM") #read in file
rownames(gcount) <- gcount$Name #makes "Name" the rowname
gcount <- gcount[,-c(1)] #drops the "Name" column
gcount <- round(gcount) #round 
dim(gcount); head(gcount)[,1:3] #view dataset attributes
gcount <- gcount[,treatmentinfo$sample]
```

Determine library size
```{r}
libSize.df <- data.frame(libSize=colSums(gcount))
```

Make DGE object
```{r}
DGEdat <- DGEList(counts=as.matrix(gcount), samples=treatmentinfo,
                  group=treatmentinfo$temp)
dim(DGEdat$counts)
```

## 2. Pre-filtering
```{r}
lib.sizes.cpm <- colSums(gcount)/1000000
lib.sizes.cpm <- tibble(sample=colnames(gcount), lib.size=lib.sizes.cpm)

keep <- rowSums(cpm(gcount) > 3.33) >= 2
table(keep)
DGEdat <- DGEdat[keep, , keep.lib.sizes=FALSE]
```

##  3. Data normalization  
```{r}
DGEdat <- calcNormFactors(DGEdat)
DGEdat$samples
```

##  4. Plot global gene expression  

Log transform the counts matrix for the next plots
```{r}
DGEdat.cpm <- DGEdat #make a copy the edgeR dataset
DGEdat.cpm$counts <- cpm(DGEdat.cpm$counts, log=TRUE, prior.count=5) #log transform the copy for the next plots
```

Run a principle coordinates analysis, all samples
```{r}
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe containing all plotting info
#d <- filter(d, temp=="Hot")
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
ncol(DGEdat.cpm$counts[,d$sample])
# Partial Least Squares Analysis
mixOmic_plsda <- plsda(t(DGEdat.cpm$counts[,d$sample]), d$timename, ncomp = 85)
plotVar(mixOmic_plsda, plot = F)
plsda_plot <- biplot(mixOmic_plsda, var.axes = TRUE, cutoff = .8, ind.names = F); plsda_plot
plotIndiv(mixOmic_plsda, ind.names = F, ellipse = TRUE, legend = TRUE)
```

Run a principle coordinates analysis, Hot samples
```{r}
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe containing all plotting info
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
d <- filter(d, temp=="Hot")
ncol(DGEdat.cpm$counts[,d$sample])
# Partial Least Squares Analysis
mixOmic_plsda <- plsda(t(DGEdat.cpm$counts[,d$sample]), d$TP_temp, ncomp = 44)
plotVar(mixOmic_plsda, plot = F)
plsda_plot <- biplot(mixOmic_plsda, var.axes = TRUE, cutoff = .8, ind.names = F); plsda_plot
plotIndiv(mixOmic_plsda, ind.names = F, ellipse = TRUE, legend = TRUE)
```

Run a principle coordinates analysis, Amb samples
```{r}
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe containing all plotting info
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
d <- filter(d, temp=="Amb")
ncol(DGEdat.cpm$counts[,d$sample])
# Partial Least Squares Analysis
mixOmic_plsda <- plsda(t(DGEdat.cpm$counts[,d$sample]), d$TP_temp, ncomp = 41)
plotVar(mixOmic_plsda, plot = F)
plsda_plot <- biplot(mixOmic_plsda, var.axes = TRUE, cutoff = .8, ind.names = F); plsda_plot
plotIndiv(mixOmic_plsda, ind.names = F, ellipse = TRUE, legend = TRUE)
```

# Emma script
```{r}
# set working directory and load necessary packages

library(reshape2)
#library(ggbiplot)
library(broom)  # devtools::install_github("tidymodels/broom")
library(cowplot)
library(ggpubr)
#library(ggfortify)
library(ggrepel)
library(gridExtra)
#library(ggforce)
# set seed
set.seed(54321)
```

Load datasets
```{r}
d <- data.frame(DGEdat.cpm$samples, name = colnames(DGEdat.cpm$counts), TP_temp=paste(DGEdat.cpm$samples$timename, DGEdat.cpm$samples$temp, sep="_")) #make a dataframe 
d$timename <- factor(d$timename, levels = c("0_hour", "6_hour", "12_hour", "30_hour", "1_week", "2_week", "4_week", "6_week", "8_week", "12_week"))
```

```{r}
#PCA
Mcap.pca.out <- prcomp(t(DGEdat.cpm$counts[,d$sample])) #, center=FALSE, scale=FALSE) #run PCA
M.summary <- summary(Mcap.pca.out); M.summary #view results
biplot(Mcap.pca.out) #plot results
```

```{r}
Mcap.pca.out %>%
  augment(d) %>% # add original dataset back in
  ggplot(aes(.fittedPC1, .fittedPC7, color = group.1)) + 
  geom_point(size = 3, color='black', aes(shape=temp, fill=group.1)) +
  scale_shape_manual(guide="legend", values=c('Amb'=21, 'Hot'=24)) +
  scale_fill_manual(guide="legend", values=c('Group2'="#FFBD5C", 'Group3'="#B873FF"#, 'Group6'="#4EB900"
                                             )) +
  #xlab(paste0("PC1: ", round(percentVar[1] * 100), "% variance")) +
  #ylab(paste0("PC7: ", round(percentVar[7] * 100), "% variance")) +
  coord_fixed() +
  theme_bw() + #Set background color
  stat_ellipse() +
  theme(panel.border = element_blank(), # Set border
        panel.grid.major = element_blank(), #Set major gridlines
        panel.grid.minor = element_blank(), #Set minor gridlines
        axis.line = element_line(colour = "black", size = 0.6), #Set axes color
        plot.background=element_blank(), #Set the plot background
        axis.title = element_text(size = 14)) + #Axis title sizet
  guides(fill = guide_legend(override.aes = list(shape=21))) +
  geom_point(size = 1.5, alpha=0.5) +
  theme_half_open(12) + background_grid()+
  stat_ellipse() +
  theme_classic() +
  #ggtitle("Montipora capitata") + 
  #facet_grid(cols=vars(timename)) +
  theme(plot.title = element_text(face = 'bold.italic', size = 14, hjust = 0)) +
  theme(legend.title = element_text(size=12, face="bold"), legend.position = "none") +
  #scale_color_manual(values = c("deepskyblue", "firebrick1"), aesthetics = c("colour")) +
  xlab("PC1 24%") + ylab("PC7 2%")

Mcap.pca.out %>%
  augment(d) %>% # add original dataset back in
  ggplot(aes(.fittedPC5, .fittedPC6, color = temp)) + 
  geom_point(size = 1.5, alpha=0.5) +
  theme_half_open(12) + background_grid()+
  stat_ellipse() +
  theme_classic() +
  #ggtitle("Montipora capitata") + 
  facet_grid(cols=vars(timename)) +
  theme(plot.title = element_text(face = 'bold.italic', size = 14, hjust = 0)) +
  theme(legend.title = element_text(size=12, face="bold"), legend.position = "none") +
  #scale_color_manual(values = c("deepskyblue", "firebrick1"), aesthetics = c("colour")) +
  xlab("PC5 23.76%") + ylab("PC6 16.09%")
```

```{r}
Mcapitata.all <- Mcap.pca.out %>%
  augment(d) %>% # add original dataset back in
  group_by(timename, temp) %>%
  mutate(PC5.mean = mean(.fittedPC5),
         PC6.mean = mean(.fittedPC6))

mcap.Hot.segments <- Mcapitata.all %>% subset(temp == "Hot") %>%
  select(timename, temp, PC5.mean, PC6.mean) %>%
  gather(variable, value, -(timename:temp)) %>%
  unite(group, timename, variable) %>% distinct() %>%
  spread(group, value)

mcap.amb.segments <- Mcapitata.all %>% subset(temp == "Amb") %>%
  select(timename, temp, PC5.mean, PC6.mean) %>%
  gather(variable, value, -(timename:temp)) %>%
  unite(group, timename, variable) %>% distinct() %>%
  spread(group, value)
```

```{r}
Mcapitata.all.fig <- ggplot(Mcapitata.all, aes(.fittedPC5, .fittedPC6, color = group.1)) + 
  geom_segment(aes(x = `0_hour_PC5.mean`, y = `0_hour_PC6.mean`, xend = `6_hour_PC5.mean`, yend = `6_hour_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 0_hour to 6_hour Hot
  geom_segment(aes(x = `6_hour_PC5.mean`, y = `6_hour_PC6.mean`, xend = `12_hour_PC5.mean`, yend = `12_hour_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 6_hour to 12_hour Hot
    geom_segment(aes(x = `12_hour_PC5.mean`, y = `12_hour_PC6.mean`, xend = `30_hour_PC5.mean`, yend = `30_hour_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 12_hour to 30_hour Hot
    geom_segment(aes(x = `30_hour_PC5.mean`, y = `30_hour_PC6.mean`, xend = `1_week_PC5.mean`, yend = `1_week_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 30_hour to 1_week Hot
      geom_segment(aes(x = `1_week_PC5.mean`, y = `1_week_PC6.mean`, xend = `2_week_PC5.mean`, yend = `2_week_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 1_week to 2_week Hot
  geom_segment(aes(x = `2_week_PC5.mean`, y = `2_week_PC6.mean`, xend = `4_week_PC5.mean`, yend = `4_week_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 2_week to 4_week Hot
    geom_segment(aes(x = `4_week_PC5.mean`, y = `4_week_PC6.mean`, xend = `6_week_PC5.mean`, yend = `6_week_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 4_week to 8_week Hot
  geom_segment(aes(x = `6_week_PC5.mean`, y = `6_week_PC6.mean`, xend = `8_week_PC5.mean`, yend = `8_week_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE) + # 4_week to 8_week Hot
  geom_segment(aes(x = `8_week_PC5.mean`, y = `8_week_PC6.mean`, xend = `12_week_PC5.mean`, yend = `12_week_PC6.mean`, colour = temp), data = mcap.Hot.segments, size=1, show.legend=FALSE, arrow = arrow()) + # 8_week to 12_week Hot
  geom_segment(aes(x = `0_hour_PC5.mean`, y = `0_hour_PC6.mean`, xend = `6_hour_PC5.mean`, yend = `6_hour_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 0_hour to 6_hour amb
  geom_segment(aes(x = `6_hour_PC5.mean`, y = `6_hour_PC6.mean`, xend = `12_hour_PC5.mean`, yend = `12_hour_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 6_hour to 12_hour amb
    geom_segment(aes(x = `12_hour_PC5.mean`, y = `12_hour_PC6.mean`, xend = `30_hour_PC5.mean`, yend = `30_hour_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 12_hour to 30_hour amb
    geom_segment(aes(x = `30_hour_PC5.mean`, y = `30_hour_PC6.mean`, xend = `1_week_PC5.mean`, yend = `1_week_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 30_hour to 1_week amb
      geom_segment(aes(x = `1_week_PC5.mean`, y = `1_week_PC6.mean`, xend = `2_week_PC5.mean`, yend = `2_week_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 1_week to 2_week amb
  geom_segment(aes(x = `2_week_PC5.mean`, y = `2_week_PC6.mean`, xend = `4_week_PC5.mean`, yend = `4_week_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 2_week to 4_week AMB
    geom_segment(aes(x = `4_week_PC5.mean`, y = `4_week_PC6.mean`, xend = `6_week_PC5.mean`, yend = `6_week_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 4_week to 8_week amb
  geom_segment(aes(x = `6_week_PC5.mean`, y = `6_week_PC6.mean`, xend = `8_week_PC5.mean`, yend = `8_week_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE) + # 4_week to 8_week amb
  geom_segment(aes(x = `8_week_PC5.mean`, y = `8_week_PC6.mean`, xend = `12_week_PC5.mean`, yend = `12_week_PC6.mean`, colour = temp), data = mcap.amb.segments, size=1, show.legend=FALSE, arrow = arrow()) + # 8_week to 12_week AMB
  geom_point(size = 1.5, alpha=0.3) +
  geom_point(aes(x=PC5.mean, y=PC6.mean), color="darkgrey") +
  geom_text(size = 2, aes(PC5.mean, PC6.mean, label=timename), vjust=-1.5, color="black") +
  theme_half_open(12) + background_grid() +
  #ggtitle("Montipora capitata") + 
  theme(legend.position = c(0.8, 0.9)) +
  theme(plot.title = element_text(face = 'bold.italic', size = 14, hjust = 0)) +
  theme(legend.title = element_text(size=12, face="bold")) +
  #scale_color_manual(values = c("deepskyblue", "firebrick1"), aesthetics = c("colour")) +
  xlab("PC5 23.76%") + ylab("PC6 16.09%"); Mcapitata.all.fig
Mcapitata.all.fig 
ggsave("Output/1c-Pacu-edgeR-allsamples-PCA-timename-arrows.pdf", Mcapitata.all.fig, width = 6, height = 5, units = c("in"))
```